Existing clinical imaging systems (e.g., X-Ray, ultrasound, MRI, PET) used for cancer detection and diagnostics arc designed to provide relatively low-resolution images of large body areas such as the breast, lungs, brain, and other similar organs and tissue. They are intended to detect potentially abnormal looking tissue structure that might represent the existence of cancer in the body. Interpreting these images to decide if an abnormality might be cancer, and therefore subjecting the patient to more testing, requires a highly skilled radiologist. Even then, this results in many false positive and some false negative interpretations due to poor spatial resolution, poor contrast, and complexity in visually assessing the image. Confirming that any abnormal looking structure in an image is in fact cancer involves performing a biopsy, removing a sample of the tissue from the body and observing it under a microscope. While cumbersome for detecting cancer, these techniques are completely impractical for cancer imaging applications such as identifying drug resistance and treatment monitoring. For these applications, current imaging systems are unable to detect detailed cellular signaling and biological processes within the tumor, and multiple biopsies through the treatment would be too invasive.
There is hence a need for molecular imaging systems, compositions, and methods capable of detecting and characterizing individual cells and biological processes within the body in a fast, minimally invasive procedure.